Feature Extraction
Transformers
Safetensors
PEFT
English
llama
llm2vec
embedding
sentence-similarity
text-encoder
llama3
kimodo
quantized
bitsandbytes
nf4
4-bit precision
lora
text-embeddings-inference
Instructions to use matbee/kimodo-llm2vec-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matbee/kimodo-llm2vec-nf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="matbee/kimodo-llm2vec-nf4")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("matbee/kimodo-llm2vec-nf4") model = AutoModel.from_pretrained("matbee/kimodo-llm2vec-nf4") - PEFT
How to use matbee/kimodo-llm2vec-nf4 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle